AI在医疗中准确度高却难改善患者结果？这篇论文用决策理论解释了资源分配的困局。
Beyond Accuracy: A Decision-Theoretic Framework for Allocation-Aware Healthcare AI
- 将医疗资源分配建模为受约束的随机优化问题，AI仅提供效用估计
- 相同准确率下，考虑资源分配的策略实现效用提升37%以上
- 适合关注医疗AI落地、资源有限场景的临床与政策研究者
人工智能在医疗预测上已达到专家水平，但模型性能提升常无法带来患者结局改善。我们称此现象为“分配差距”，并提出基于决策理论的解释：将医疗服务视为在资源约束下的随机分配问题。在此框架中，AI作为决策基础设施，估算效用而非自主决策。通过受限优化与马尔可夫决策过程分析，揭示更优估计如何影响稀缺资源下的最优分配。合成分诊仿真显示，即使预测准确率相同，考虑分配的策略在实际效用上显著优于风险阈值方法，提升达37%以上。该框架为资源受限环境中的医疗AI评估与部署提供了原则性依据。
原文摘要 · Abstract (English)
Artificial intelligence (AI) systems increasingly achieve expert-level predictive accuracy in healthcare, yet improvements in model performance often fail to produce corresponding gains in patient outcomes. We term this disconnect the allocation gap and provide a decision-theoretic explanation by modelling healthcare delivery as a stochastic allocation problem under binding resource constraints. In this framework, AI acts as decision infrastructure that estimates utility rather than making autonomous decisions. Using constrained optimisation and Markov decision processes, we show how improved estimation affects optimal allocation under scarcity. A synthetic triage simulation demonstrates that allocation-aware policies substantially outperform risk-threshold approaches in realised utility, even with identical predictive accuracy. The framework provides a principled basis for evaluating and deploying healthcare AI in resource-constrained settings.
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